{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# CatBoost Algorithms Part 2"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "CatBoost (categorical boosting) is an algorithm for gradient boosting on decision trees. Is a machine learning technique created by Yandex; therefore, it outperforms many existing boosting such as XGBoost and Light GBM. "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:09.087Z",
          "iopub.execute_input": "2023-03-08T02:38:09.092Z",
          "shell.execute_reply": "2023-03-08T02:38:09.638Z",
          "iopub.status.idle": "2023-03-08T02:38:09.644Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2007-01-01'\n",
        "end = '2018-11-16'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "                                Open       High        Low      Close  \\\nDate                                                                    \n2007-01-03 00:00:00-05:00  20.080000  20.400000  19.350000  19.520000   \n2007-01-04 00:00:00-05:00  19.660000  19.860001  19.320000  19.790001   \n2007-01-05 00:00:00-05:00  19.540001  19.910000  19.540001  19.709999   \n2007-01-08 00:00:00-05:00  19.709999  19.860001  19.370001  19.469999   \n2007-01-09 00:00:00-05:00  19.450001  19.709999  19.370001  19.650000   \n\n                           Adj Close    Volume  \nDate                                            \n2007-01-03 00:00:00-05:00  19.520000  28350300  \n2007-01-04 00:00:00-05:00  19.790001  23652500  \n2007-01-05 00:00:00-05:00  19.709999  15902400  \n2007-01-08 00:00:00-05:00  19.469999  15814800  \n2007-01-09 00:00:00-05:00  19.650000  14494200  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2007-01-03 00:00:00-05:00</th>\n      <td>20.080000</td>\n      <td>20.400000</td>\n      <td>19.350000</td>\n      <td>19.520000</td>\n      <td>19.520000</td>\n      <td>28350300</td>\n    </tr>\n    <tr>\n      <th>2007-01-04 00:00:00-05:00</th>\n      <td>19.660000</td>\n      <td>19.860001</td>\n      <td>19.320000</td>\n      <td>19.790001</td>\n      <td>19.790001</td>\n      <td>23652500</td>\n    </tr>\n    <tr>\n      <th>2007-01-05 00:00:00-05:00</th>\n      <td>19.540001</td>\n      <td>19.910000</td>\n      <td>19.540001</td>\n      <td>19.709999</td>\n      <td>19.709999</td>\n      <td>15902400</td>\n    </tr>\n    <tr>\n      <th>2007-01-08 00:00:00-05:00</th>\n      <td>19.709999</td>\n      <td>19.860001</td>\n      <td>19.370001</td>\n      <td>19.469999</td>\n      <td>19.469999</td>\n      <td>15814800</td>\n    </tr>\n    <tr>\n      <th>2007-01-09 00:00:00-05:00</th>\n      <td>19.450001</td>\n      <td>19.709999</td>\n      <td>19.370001</td>\n      <td>19.650000</td>\n      <td>19.650000</td>\n      <td>14494200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:09.649Z",
          "iopub.execute_input": "2023-03-08T02:38:09.652Z",
          "shell.execute_reply": "2023-03-08T02:38:10.120Z",
          "iopub.status.idle": "2023-03-08T02:38:10.019Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.index = pd.Index(dataset.index.date, name=\"Date\")\n",
        "dataset"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close     Volume\nDate                                                                        \n2007-01-03  20.080000  20.400000  19.350000  19.520000  19.520000   28350300\n2007-01-04  19.660000  19.860001  19.320000  19.790001  19.790001   23652500\n2007-01-05  19.540001  19.910000  19.540001  19.709999  19.709999   15902400\n2007-01-08  19.709999  19.860001  19.370001  19.469999  19.469999   15814800\n2007-01-09  19.450001  19.709999  19.370001  19.650000  19.650000   14494200\n...               ...        ...        ...        ...        ...        ...\n2018-11-09  20.770000  21.190001  20.110001  21.030001  21.030001   85900700\n2018-11-12  20.680000  20.850000  18.799999  19.030001  19.030001   95948200\n2018-11-13  19.280001  20.020000  18.969999  19.610001  19.610001   76126000\n2018-11-14  20.180000  21.110001  19.760000  20.809999  20.809999  106344300\n2018-11-15  20.719999  21.770000  20.420000  21.490000  21.490000   97715500\n\n[2991 rows x 6 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2007-01-03</th>\n      <td>20.080000</td>\n      <td>20.400000</td>\n      <td>19.350000</td>\n      <td>19.520000</td>\n      <td>19.520000</td>\n      <td>28350300</td>\n    </tr>\n    <tr>\n      <th>2007-01-04</th>\n      <td>19.660000</td>\n      <td>19.860001</td>\n      <td>19.320000</td>\n      <td>19.790001</td>\n      <td>19.790001</td>\n      <td>23652500</td>\n    </tr>\n    <tr>\n      <th>2007-01-05</th>\n      <td>19.540001</td>\n      <td>19.910000</td>\n      <td>19.540001</td>\n      <td>19.709999</td>\n      <td>19.709999</td>\n      <td>15902400</td>\n    </tr>\n    <tr>\n      <th>2007-01-08</th>\n      <td>19.709999</td>\n      <td>19.860001</td>\n      <td>19.370001</td>\n      <td>19.469999</td>\n      <td>19.469999</td>\n      <td>15814800</td>\n    </tr>\n    <tr>\n      <th>2007-01-09</th>\n      <td>19.450001</td>\n      <td>19.709999</td>\n      <td>19.370001</td>\n      <td>19.650000</td>\n      <td>19.650000</td>\n      <td>14494200</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2018-11-09</th>\n      <td>20.770000</td>\n      <td>21.190001</td>\n      <td>20.110001</td>\n      <td>21.030001</td>\n      <td>21.030001</td>\n      <td>85900700</td>\n    </tr>\n    <tr>\n      <th>2018-11-12</th>\n      <td>20.680000</td>\n      <td>20.850000</td>\n      <td>18.799999</td>\n      <td>19.030001</td>\n      <td>19.030001</td>\n      <td>95948200</td>\n    </tr>\n    <tr>\n      <th>2018-11-13</th>\n      <td>19.280001</td>\n      <td>20.020000</td>\n      <td>18.969999</td>\n      <td>19.610001</td>\n      <td>19.610001</td>\n      <td>76126000</td>\n    </tr>\n    <tr>\n      <th>2018-11-14</th>\n      <td>20.180000</td>\n      <td>21.110001</td>\n      <td>19.760000</td>\n      <td>20.809999</td>\n      <td>20.809999</td>\n      <td>106344300</td>\n    </tr>\n    <tr>\n      <th>2018-11-15</th>\n      <td>20.719999</td>\n      <td>21.770000</td>\n      <td>20.420000</td>\n      <td>21.490000</td>\n      <td>21.490000</td>\n      <td>97715500</td>\n    </tr>\n  </tbody>\n</table>\n<p>2991 rows × 6 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.024Z",
          "iopub.execute_input": "2023-03-08T02:38:10.029Z",
          "iopub.status.idle": "2023-03-08T02:38:10.037Z",
          "shell.execute_reply": "2023-03-08T02:38:10.123Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume  \\\nDate                                                                          \n2007-01-04  19.660000  19.860001  19.320000  19.790001  19.790001  23652500   \n2007-01-05  19.540001  19.910000  19.540001  19.709999  19.709999  15902400   \n2007-01-08  19.709999  19.860001  19.370001  19.469999  19.469999  15814800   \n2007-01-09  19.450001  19.709999  19.370001  19.650000  19.650000  14494200   \n2007-01-10  19.639999  20.020000  19.500000  20.010000  20.010000  19783200   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                 \n2007-01-04                  0                 0         0  0.013832  \n2007-01-05                  0                 1         0 -0.004043  \n2007-01-08                  0                 0         1 -0.012177  \n2007-01-09                  1                 1         1  0.009245  \n2007-01-10                  1                 1         1  0.018321  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2007-01-04</th>\n      <td>19.660000</td>\n      <td>19.860001</td>\n      <td>19.320000</td>\n      <td>19.790001</td>\n      <td>19.790001</td>\n      <td>23652500</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.013832</td>\n    </tr>\n    <tr>\n      <th>2007-01-05</th>\n      <td>19.540001</td>\n      <td>19.910000</td>\n      <td>19.540001</td>\n      <td>19.709999</td>\n      <td>19.709999</td>\n      <td>15902400</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-0.004043</td>\n    </tr>\n    <tr>\n      <th>2007-01-08</th>\n      <td>19.709999</td>\n      <td>19.860001</td>\n      <td>19.370001</td>\n      <td>19.469999</td>\n      <td>19.469999</td>\n      <td>15814800</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.012177</td>\n    </tr>\n    <tr>\n      <th>2007-01-09</th>\n      <td>19.450001</td>\n      <td>19.709999</td>\n      <td>19.370001</td>\n      <td>19.650000</td>\n      <td>19.650000</td>\n      <td>14494200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.009245</td>\n    </tr>\n    <tr>\n      <th>2007-01-10</th>\n      <td>19.639999</td>\n      <td>20.020000</td>\n      <td>19.500000</td>\n      <td>20.010000</td>\n      <td>20.010000</td>\n      <td>19783200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.018321</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.042Z",
          "iopub.execute_input": "2023-03-08T02:38:10.046Z",
          "iopub.status.idle": "2023-03-08T02:38:10.056Z",
          "shell.execute_reply": "2023-03-08T02:38:10.125Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "(2990, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.062Z",
          "iopub.execute_input": "2023-03-08T02:38:10.066Z",
          "iopub.status.idle": "2023-03-08T02:38:10.075Z",
          "shell.execute_reply": "2023-03-08T02:38:10.127Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low', 'Volume']].values\n",
        "y = dataset['Buy_Sell'].values"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.081Z",
          "iopub.execute_input": "2023-03-08T02:38:10.084Z",
          "iopub.status.idle": "2023-03-08T02:38:10.090Z",
          "shell.execute_reply": "2023-03-08T02:38:10.130Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import catboost as cb\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "# from sklearn.model_selection import GridSearchCV"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.098Z",
          "iopub.execute_input": "2023-03-08T02:38:10.101Z",
          "shell.execute_reply": "2023-03-08T02:38:10.622Z",
          "iopub.status.idle": "2023-03-08T02:38:10.626Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.631Z",
          "iopub.execute_input": "2023-03-08T02:38:10.635Z",
          "iopub.status.idle": "2023-03-08T02:38:10.640Z",
          "shell.execute_reply": "2023-03-08T02:38:10.659Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cb = CatBoostClassifier()\n",
        "cb.fit(X_train, y_train)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.014951\n",
            "0:\tlearn: 0.6929853\ttotal: 142ms\tremaining: 2m 21s\n",
            "1:\tlearn: 0.6928677\ttotal: 144ms\tremaining: 1m 11s\n",
            "2:\tlearn: 0.6926973\ttotal: 145ms\tremaining: 48.2s\n",
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            "9:\tlearn: 0.6918812\ttotal: 156ms\tremaining: 15.5s\n",
            "10:\tlearn: 0.6917209\ttotal: 158ms\tremaining: 14.2s\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "<catboost.core.CatBoostClassifier at 0x25bc68a4e48>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:10.647Z",
          "iopub.execute_input": "2023-03-08T02:38:10.650Z",
          "iopub.status.idle": "2023-03-08T02:38:12.626Z",
          "shell.execute_reply": "2023-03-08T02:38:12.805Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Model is fitted: ' + str(cb.is_fitted()))\n",
        "print('Model params:')\n",
        "print(cb.get_params())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model is fitted: True\n",
            "Model params:\n",
            "{}\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:12.636Z",
          "iopub.execute_input": "2023-03-08T02:38:12.641Z",
          "iopub.status.idle": "2023-03-08T02:38:12.654Z",
          "shell.execute_reply": "2023-03-08T02:38:12.810Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = cb.predict(X_test)"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:12.662Z",
          "iopub.execute_input": "2023-03-08T02:38:12.666Z",
          "iopub.status.idle": "2023-03-08T02:38:12.674Z",
          "shell.execute_reply": "2023-03-08T02:38:12.819Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
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          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
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        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:12.683Z",
          "iopub.execute_input": "2023-03-08T02:38:12.690Z",
          "iopub.status.idle": "2023-03-08T02:38:12.701Z",
          "shell.execute_reply": "2023-03-08T02:38:12.822Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('CatBoost Score:', cb.score(X_test, y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CatBoost Score: 0.520066889632107\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2023-03-08T02:38:12.707Z",
          "iopub.execute_input": "2023-03-08T02:38:12.710Z",
          "iopub.status.idle": "2023-03-08T02:38:12.721Z",
          "shell.execute_reply": "2023-03-08T02:38:12.825Z"
        }
      }
    }
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